Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models

Cuihua Zou; Li Su; Mika Pan; Liechun Chen; Hepeng Li; Chun Zou; Jieqiong Xie; Xiaohua Huang; Mengru Lu; Donghua Zou

doi:10.3389/fnagi.2023.1079433

Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models

Front Aging Neurosci. 2023 Feb 16:15:1079433. doi: 10.3389/fnagi.2023.1079433. eCollection 2023.

Authors

Cuihua Zou¹, Li Su², Mika Pan³, Liechun Chen³, Hepeng Li³, Chun Zou³, Jieqiong Xie³, Xiaohua Huang², Mengru Lu³, Donghua Zou^{3

4}

Affiliations

¹ Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China.
² Department of Neurology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.
³ Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.
⁴ Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.

Abstract

Background: Despite tremendous progress in diagnosis and prediction of Alzheimer's disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment.

Methods: By differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients.

Results: Screened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients.

Conclusion: The LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

Keywords: Alzheimer’s disease; biomarkers; gradient boosting machine; least absolute shrinkage and selection operator; logistic regression model; random forest model.